scholarly journals Identification of neural oscillations and epileptiform changes in human brain organoids

2019 ◽  
Author(s):  
Ranmal A. Samarasinghe ◽  
Osvaldo A. Miranda ◽  
Simon Mitchell ◽  
Isabella Ferando ◽  
Momoko Watanabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Network Architecture ◽  
Network Formation ◽  
Neurological Diseases ◽  
Extracellular Recording ◽  
Brain Network ◽  
Network Activity ◽  
Intact Brain ◽  
Oscillatory Network

ABSTRACTHuman brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

scholarly journals Epilepsy Research Now in 3D: Harnessing the Power of Brain Organoids in Epilepsy

2022 ◽  
pp. 153575972110703
Author(s):  
Christina Gross
Keyword(s):  
Pluripotent Stem Cells ◽  
Network Formation ◽  
Neurological Diseases ◽  
Extracellular Recording ◽  
Brain Network ◽  
Calcium Sensor ◽  
Brain Growth ◽  
Anatomical Changes ◽  
Intact Brain ◽  
Induced Pluripotent

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

scholarly journals Energy constraints on brain network formation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kosuke Takagi
Keyword(s):  
Network Formation ◽  
Cost Minimization ◽  
Brain Network ◽  
Network Activity ◽  
Rich Functionality ◽  
Energy Constraints ◽  
Dependent Form ◽  
Statistical Similarity ◽  
Simple Ratio ◽  
The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals Progesterone modulates theta oscillations in the frontal-parietal network

2020 ◽  
Author(s):  
Justin Riddle ◽  
Sangtae Ahn ◽  
Trevor McPherson ◽  
Susan Girdler ◽  
Flavio Frohlich
Keyword(s):  
Cognitive Control ◽  
Brain Activity ◽  
Specific Effect ◽  
Brain Network ◽  
Theta Oscillations ◽  
Network Activity ◽  
Top Down ◽  
Frontal Midline Theta ◽  
Phase Amplitude ◽  
Oscillatory Network

AbstractThe neuroactive metabolites of the steroid hormones progesterone (P4) and testosterone (T) are GABAergic modulators that influence cognitive control, yet the specific effect of P4 and T on brain network activity remains poorly understood. Here, we investigated if a fundamental oscillatory network activity pattern related to cognitive control, frontal midline theta (FMT) oscillations, are modulated by steroids hormones, P4 and T. We measured the concentration P4 and T using salivary enzyme immunoassay and FMT oscillations using high-density electroencephalography (EEG) during the eyes-open resting state in fifty-five healthy female and male participants. Electrical brain activity was analyzed using Morlet wavelet convolution, beamformer source localization, background noise spectral fitting, and phase amplitude coupling analysis. Steroid hormone concentrations and biological sex were used as predictors for scalp and source-estimated theta oscillations and for top-down theta-gamma phase amplitude coupling. Elevated concentrations of P4 predicted increased FMT oscillatory amplitude across both sexes, and no relationship was found with T. The positive correlation with P4 was specific to the frontal-midline electrodes and survived correction for the background noise of the brain. Using source localization, FMT oscillations were localized to the frontal-parietal network. Additionally, theta amplitude within the frontal-parietal network, but not the default mode network, positively correlated with P4 concentration. Finally, P4 concentration correlated with increased coupling between FMT phase and posterior gamma amplitude. Our results suggest that P4 concentration modulates brain activity via upregulation of theta oscillations in the frontal-parietal network and increased top-down control over posterior cortical sites.Significance StatementThe neuroactive metabolites of the steroid hormones progesterone (P4) and testosterone (T) are GABAergic modulators that influence cognitive control, yet the specific effect of P4 and T on brain network activity remains poorly understood. Here, we investigated if a fundamental oscillatory network activity pattern related to cognitive control, frontal midline theta (FMT) oscillations, are modulated by steroids hormones, P4 and T. Our results suggest that P4 concentration modulates brain activity via upregulation of theta oscillations in the frontal-parietal network and increased top-down control over posterior cortical sites.

scholarly journals Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis

2018 ◽  
Author(s):  
Luke J. Hearne ◽  
Ryan J. Dean ◽  
Gail A. Robinson ◽  
Linda J. Richards ◽  
Jason B. Mattingley ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Neural Activity ◽  
Network Architecture ◽  
Cognitive Complexity ◽  
Cognitive Task ◽  
Brain Network ◽  
Task Demands ◽  
Network Activity ◽  
Network Analyses ◽  
Cognitive Reasoning

AbstractCognitive reasoning is thought to require functional interactions between whole-brain networks. Such networks rely on both cerebral hemispheres, with the corpus callosum providing cross-hemispheric communication. Here we used high-field functional magnetic resonance imaging (7T fMRI), a well validated cognitive task, and brain network analyses to investigate the functional networks underlying cognitive reasoning in individuals with corpus callosum dysgenesis (CCD), an anatomical abnormality that affects the corpus callosum. Participants with CCD were asked to solve cognitive reasoning problems while their brain activity was measured using fMRI. The complexity of these problems was parametrically varied by changing the complexity of relations that needed to be established between shapes within each problem matrix. Behaviorally, participants showed a typical reduction in task performance as problem complexity increased. Task-evoked neural activity was observed in brain regions known to constitute two key cognitive control systems: the fronto-parietal and cingulo-opercular networks. Under low complexity demands, network topology and the patterns of local neural activity in the CCD group closely resembled those observed in neurotypical controls. By contrast, when asked to solve more complex problems, participants with CCD showed a reduction in neural activity and connectivity within the fronto-parietal network. These complexity-induced, as opposed to resting-state, differences in functional network activity help resolve the apparent paradox between preserved network architecture found at rest in CCD individuals, and the heterogeneous deficits they display in response to cognitive task demands [preprint: https://doi.org/10.1101/312629].

scholarly journals Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

2019 ◽  
Author(s):  
João F. Guassi Moreira ◽  
Katie A. McLaughlin ◽  
Jennifer A. Silvers
Keyword(s):  
Emotion Regulation ◽  
Skill Acquisition ◽  
Network Architecture ◽  
Evolutionary Biology ◽  
Brain Activity ◽  
Brain Networks ◽  
Adult Life ◽  
Brain Network ◽  
Network Activity ◽  
Whole Brain

AbstractThe ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of emotion regulation skill acquisition while accounting for age, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in a sample of youth (N = 70, 34 female). We were able to predict emotion regulation ability, but not age, using network activity metrics from whole-brain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, suggesting that brain activity during emotion regulation encodes unique information about regulatory skill acquisition in youth but not domain-general maturation.Significance StatementThe acquisition of emotion regulation is critical for healthy functioning in later adult life. To date, little is known about how brain networks support the developmental acquisition of emotion regulation skills. This is noteworthy because brain networks have been increasingly shown to provide highly useful information about neural activity. Here we show that brain activity during an emotion regulation task encodes information about regulatory abilities over and above age. These results suggest emotion regulation skills are dependent on neural specialization of domain-specific systems, whereas age is encoded via domain-general systems.

scholarly journals Nested oscillatory dynamics in cortical organoids model early human brain network development

2018 ◽  
Author(s):  
Cleber A. Trujillo ◽  
Richard Gao ◽  
Priscilla D. Negraes ◽  
Isaac A. Chaim ◽  
Alain Domissy ◽  
...  
Keyword(s):  
Human Brain ◽  
Network Activity ◽  
Brain Maturation ◽  
Oscillatory Activity ◽  
List Type ◽  
Complex Activity ◽  
Neuronal Computation ◽  
Oscillatory Network

SUMMARYStructural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. Here, we developed cortical organoids that spontaneously display periodic and regular oscillatory network events that are dependent on glutamatergic and GABAergic signaling. These nested oscillations exhibit cross-frequency coupling, proposed to coordinate neuronal computation and communication. As evidence of potential network maturation, oscillatory activity subsequently transitioned to more spatiotemporally irregular patterns, capturing features observed in preterm human electroencephalography (EEG). These results show that the development of structured network activity in the human neocortex may follow stable genetic programming, even in the absence of external or subcortical inputs. Our approach provides novel opportunities for investigating and manipulating the role of network activity in the developing human cortex.HIGHLIGHTSEarly development of human functional neural networks and oscillatory activity can be modeled in vitro.Cortical organoids exhibit phase-amplitude coupling between delta oscillation (2 Hz) and high-frequency activity (100-400 Hz) during network-synchronous events.Differential role of glutamate and GABA in initiating and maintaining oscillatory network activity.Developmental impairment of MECP2-KO cortical organoids impacts the emergence of oscillatory activity.Cortical organoid network electrophysiological signatures correlate with human preterm neonatal EEG features.eTOCBrain oscillations are a candidate mechanism for how neural populations are temporally organized to instantiate cognition and behavior. Cortical organoids initially exhibit periodic and highly regular nested oscillatory network events that eventually transition to more spatiotemporally complex activity, capturing features of late-stage preterm infant electroencephalography. Functional neural circuitry in cortical organoids exhibits emergence and development of oscillatory network dynamics similar to those found in the developing human brain.

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